The design of medical treatments in treatment versus control (standard treatment or placebo) studies in currently hampered by nonuse of a voluminous and rapidly growing statistical methods literature. This is due to serious deficiencies in the statistical methods, rendering them unsuitable for application. The purpose of this research is to develop a treatment versus control statistical model which is sufficiently flexible to allow adaptation to, and provide useful methods for, accurate statistical analysis of human and animal tumor studies. A convincing test of the null hypothesis will be included, while the hopeless-to-specify "patient horizon" will not. Selection bias will be controlled by partial randomization. Ethical cost due to delay in reporting results, and the implementation ease of relatively simple methods, lead us to a two-stage analysis, formulated to capture the best of fixed-sample simplicity and sequential efficiency. Unequal unknown response variable variances (heteroscedasticity) will be allowed for. Emphasis will be on realistic modeling as well as on statistical accuracy.